Trade Off Analysis and Decision Frameworks Questions
Covers the practice of structured trade off evaluation and repeatable decision processes across product and technical domains. Topics include enumerating alternatives, defining evaluation criteria such as cost risk time to market and user impact, building scoring matrices and weighted models, running sensitivity or scenario analysis, documenting assumptions, surfacing constraints, and communicating clear recommendations with mitigation plans. Interviewers will assess the candidate's ability to justify choices logically, quantify impacts when possible, and explain governance or escalation mechanisms used to make consistent decisions.
EasyTechnical
0 practiced
You have three options and two scenarios (best-case and worst-case). Given these hypothetical normalized scores (0–1): Option A [0.8, 0.6], Option B [0.75, 0.7], Option C [0.7, 0.9] under scenario changes of a single criterion (e.g., cost swings), explain which option is most robust and why. Describe how you would present this robustness analysis to stakeholders.
MediumTechnical
0 practiced
Design an audit-friendly Decision Record template (fields and short descriptions) that captures trade-offs for an AI architecture choice. Include fields for alternatives, evaluation criteria, weights, sensitivity analysis summary, assumptions, constraints, owners, approval signatures, and post-deployment monitoring plan. Explain why each field is necessary for governance and future audits.
MediumTechnical
0 practiced
You are choosing between two monitoring strategies for serving ML models: lightweight black-box end-to-end metrics (latency, error rate, business KPIs) versus heavy white-box telemetry (layer activations, embedding drift, per-feature distributions). Compare them across cost, detection speed for different failure modes, and operational burden. Propose a hybrid monitoring approach and explain how you'd justify its costs.
HardTechnical
0 practiced
Your organization's inference bill is $1M/month. Propose a quantitative decision framework to choose among these cost-reduction options: model pruning, serving cold requests on CPU, result caching, scheduled pre-warming with cheaper instances, or moving less-critical traffic to preemptible GPUs. For each option, estimate deployment effort, expected cost savings over 12 months, performance risk, and operational complexity. Show how you'd compute ROI and prioritize experiments.
HardSystem Design
0 practiced
Design a weighted decision model plus sensitivity analysis to decide on eventual consistency versus strong consistency for cross-region feature updates feeding a real-time recommendation engine. Specify how you'd quantify user-impact (e.g., revenue-at-risk), business risk (e.g., inconsistency-induced errors), and operational complexity, then show how sensitivity to weight changes could flip the recommendation.
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